Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods

Though bioenergy still emits some emissions, they are a lot lower than fossil fuels. Besides, the increase in water and power consumption keeps pace with the earth's growing population. Therefore, many studies have been conducted on multi-purpose cycles. Utilizing the biomass gasification proce...

Full description

Bibliographic Details
Published in:Fuel
Main Author: Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
Format: Article
Language:English
Published: Elsevier Ltd 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142825735&doi=10.1016%2fj.fuel.2022.126494&partnerID=40&md5=47a370af56f91a42b152399ee97fbb23
id 2-s2.0-85142825735
spelling 2-s2.0-85142825735
Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
2023
Fuel
334

10.1016/j.fuel.2022.126494
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142825735&doi=10.1016%2fj.fuel.2022.126494&partnerID=40&md5=47a370af56f91a42b152399ee97fbb23
Though bioenergy still emits some emissions, they are a lot lower than fossil fuels. Besides, the increase in water and power consumption keeps pace with the earth's growing population. Therefore, many studies have been conducted on multi-purpose cycles. Utilizing the biomass gasification process to produce the fuel needed for a gas turbine is a novel technology. The additional heat from the outlet gases is used to produce higher power in the Rankin cycle and cooling in the double-effect absorption chiller. The net power produced in this cycle will be used to empower the desalination system using reverse osmosis (RO) to increase the inlet pressure of the salty water so that it passes the water treatment membranes. Since the outlet water pressure is high, a water turbine is used to generate electricity. The genetic algorithm, along with machine learning methods, is used to achieve the optimal performance conditions and reduce the calculational time; because the time and calculational costs for modeling every cycle are high, and the optimization process will be prolonged. The results revealed that the proposed system is capable of producing a power of nearly 400 kW, with an exergy efficiency of 41 % and CO2 emission rate of 0.59 ton/MWh. Besides, the desalination rate and cooling capacities are 1.7 kg/s and 310 kW, respectively. © 2022 Elsevier Ltd
Elsevier Ltd
162361
English
Article

author Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
spellingShingle Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
author_facet Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
author_sort Hai T.; Ashraf Ali M.; Zhou J.; A. Dhahad H.; Goyal V.; Fahad Almojil S.; Ibrahim Almohana A.; Fahmi Alali A.; Twfiq Almoalimi K.; Najat Ahmed A.
title Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
title_short Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
title_full Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
title_fullStr Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
title_full_unstemmed Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
title_sort Feasibility and environmental assessments of a biomass gasification-based cycle next to optimization of its performance using artificial intelligence machine learning methods
publishDate 2023
container_title Fuel
container_volume 334
container_issue
doi_str_mv 10.1016/j.fuel.2022.126494
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142825735&doi=10.1016%2fj.fuel.2022.126494&partnerID=40&md5=47a370af56f91a42b152399ee97fbb23
description Though bioenergy still emits some emissions, they are a lot lower than fossil fuels. Besides, the increase in water and power consumption keeps pace with the earth's growing population. Therefore, many studies have been conducted on multi-purpose cycles. Utilizing the biomass gasification process to produce the fuel needed for a gas turbine is a novel technology. The additional heat from the outlet gases is used to produce higher power in the Rankin cycle and cooling in the double-effect absorption chiller. The net power produced in this cycle will be used to empower the desalination system using reverse osmosis (RO) to increase the inlet pressure of the salty water so that it passes the water treatment membranes. Since the outlet water pressure is high, a water turbine is used to generate electricity. The genetic algorithm, along with machine learning methods, is used to achieve the optimal performance conditions and reduce the calculational time; because the time and calculational costs for modeling every cycle are high, and the optimization process will be prolonged. The results revealed that the proposed system is capable of producing a power of nearly 400 kW, with an exergy efficiency of 41 % and CO2 emission rate of 0.59 ton/MWh. Besides, the desalination rate and cooling capacities are 1.7 kg/s and 310 kW, respectively. © 2022 Elsevier Ltd
publisher Elsevier Ltd
issn 162361
language English
format Article
accesstype
record_format scopus
collection Scopus
_version_ 1809678017367113728